In optimal path following problems the motion along a given geometric path is optimized according to a desired objective while accounting for the system dynamics and system constraints. In the case of time-optimal path following, for example, the system input to move along the geometric path in minimal time is computed. In practice however, due to model-plant mismatch, (i) the geometric path is not followed exactly, and (ii) the optimized trajectory might be suboptimal, or even infeasible for the true plant. Assuming that the system performs the task repeatedly, this paper proposes an iterative learning control approach to improve the path following performance. The proposed learning algorithm is experimentally validated for a time-optimal path following problem on an XY-table. The results show that the developed ILC approach improves both the execution time and the accuracy significantly.